The present invention relates to a method for data reduction of digital picture signals by vector quantization of coefficients, acquired by orthonormal transformation by means of a symmetrical, nearly cyclical Hadamard matrix, in which an incoming picture signal is divided into blocks before the transformation, the blocks are reordered into vectors according to a Peano curve for acquiring an input vector, and a mean value coefficient and a plurality of structure coefficients are formed by the transformation.
In recent years, great interest has arisen for data reduction of pictures, for example, for employment for video moving pictures within new digital communication networks, for example the ISDN (Integrated Services Digital Network). Areas of application for this, for example, lie in the field of what is referred to as tele-conferencing or of picture telephones at transmission rates of 2 Mbit/s down to 64 kbit/s.
Method for data reduction of digital picture signals which use vector quantization, with a spatial and temporal grouping of the picture elements, have proven promising for achieving good results. Vector quantization seems inherently superior to other, known source coding methods, since the rate distortion theory indicates a data reduction with optimum results, provided that the vector dimension is sufficiently high.
Vector quantization regards to a block of successive samples, for example, of a picture, as a vector which is quantized as a unit. In contrast to a scalar quantization, vector quantization takes the statistical dependencies of the samples into consideration.
A vector quantizer seeks a k-dimensional vector, from a finite set of output vectors, which exhibits the greatest similarity to the input signal. By means of what is referred to as a code book, this vector is coded with a binary code word having the length L=log.sub.2 N, where N indicates the number of the output vectors or the size of the code book. Differing from scalar quantization, the plurality R=L/k of the bits which are required in order to code a vector component can be a fraction of one.
The main hurdle in employing vector quantization is its complexity, which rises exponentially with R and k, i.e. a vector quantizer of the size k which works at a rate of Rbits/component requires k2.sup.Rk operations for coding, and likewise requires a code book memory size of the same order. In most applications for vector quantizing, therefore, the block size k is limited to 16. Various problems have arisen, particularly with such small block sizes, since the vector quantizing tends to generate the occurrence of a highly visible noise in the proximity of the block boundaries of a decoded picture. These quantizing errors are of basically two types, namely,
formation of step-shaped edges, caused by independent coding of the picture blocks; and
sudden changes of the gray scale from block to block, whereby the gray scales of the original picture gradually change (block contouring).